The Strands Agents MCP Server is a model-driven approach to building AI agents in just a few lines of code. It provides curated documentation access to GenAI tools via llms.txt files, enabling AI coding assistants to search and retrieve relevant documentation with intelligent ranking. Features include smart document search, curated content indexing, on-demand fetching, snippet generation, and real URL support. The server can be used with various applications that support MCP servers, such as Amazon Q Developer CLI, Anthropic Claude Code, Cline, and Cursor. Users can quickly test the MCP server using the MCP Inspector and follow the provided steps to configure their MCP client and start using the documentation tools. The project welcomes contributions and is licensed under the Apache License 2.0.

RAG-Powered Odoo Documentation Assistant is a comprehensive documentation processing and chat system that converts Odoo's documentation to a searchable knowledge base with an AI-powered chat interface. It supports multiple Odoo versions (16.0, 17.0, 18.0) and provides semantic search capabilities powered by OpenAI embeddings. The tool automates the conversion of RST to Markdown, offers real-time semantic search, context-aware AI-powered chat responses, and multi-version support. It includes a Streamlit-based web UI, REST API for programmatic access, and a CLI for document processing and chat. The system operates through a pipeline of data processing steps and an interface layer for UI and API access to the knowledge base.

MetaAgent is a multi-agent collaboration platform designed to build, manage, and deploy multi-modal AI agents without the need for coding. Users can easily create AI agents by editing a yml file or using the provided UI. The platform supports features such as building LLM-based AI agents, multi-modal interactions with users using texts, audios, images, and videos, creating a company of agents for complex tasks like drawing comics, vector database and knowledge embeddings, and upcoming features like UI for creating and using AI agents, fine-tuning, and RLHF. The tool simplifies the process of creating and deploying AI agents for various tasks.

The starknet-agent-kit is a toolkit designed for creating AI agents that can interact with the Starknet blockchain. It provides support for multiple AI providers such as Anthropic, OpenAI, Google Gemini, and Ollama. The kit includes an NPM package and a NestJS server with a web interface. Users can run the server in different modes like Chat Mode for conversations, checking balances, executing transfers, and managing accounts, as well as Autonomous Mode for automated monitoring. Additionally, the kit offers a library mode for more advanced usage, allowing users to interact with the StarknetAgent class for executing specific actions. The kit aims to simplify the process of integrating AI capabilities with blockchain interactions.

Avalon-LLM is a repository containing the official code for AvalonBench and the Avalon agent Strategist. AvalonBench evaluates Large Language Models (LLMs) playing The Resistance: Avalon, a board game requiring deductive reasoning, coordination, collaboration, and deception skills. Strategist utilizes LLMs to learn strategic skills through self-improvement, including high-level strategic evaluation and low-level execution guidance. The repository provides instructions for running AvalonBench, setting up Strategist, and conducting experiments with different agents in the game environment.

Run your own AI cluster at home with everyday devices. Exo is experimental software that unifies existing devices into a powerful GPU, supporting wide model compatibility, dynamic model partitioning, automatic device discovery, ChatGPT-compatible API, and device equality. It does not use a master-worker architecture, allowing devices to connect peer-to-peer. Exo supports different partitioning strategies like ring memory weighted partitioning. Installation is recommended from source. Documentation includes example usage on multiple MacOS devices and information on inference engines and networking modules. Known issues include the iOS implementation lagging behind Python.

A Model Context Protocol (MCP) server that enables secure interaction with MySQL databases. This server allows AI assistants to list tables, read data, and execute SQL queries through a controlled interface, making database exploration and analysis safer and more structured. It provides features such as listing available MySQL tables as resources, reading table contents, executing SQL queries with proper error handling, secure database access through environment variables, and comprehensive logging. The tool ensures security best practices by never committing environment variables or credentials, using a database user with minimal required permissions, implementing query whitelisting for production use, and monitoring and logging all database operations.

Gemini Coder is a free 2M context AI coding assistant that allows users to conveniently copy folders and files for chatbots. It provides FIM completions, file refactoring, and AI-suggested changes. The extension is versatile, private, and lightweight, offering unmatched accuracy, speed, and cost in AI assistance. Users have full control over the context and coding conventions included, ensuring high performance and signal to noise ratio. Gemini Coder supports various chatbots and provides quick start guides for chat and FIM completions. It also offers commands for FIM completions, refactoring, applying changes, chat, and context copying. Users can set up custom model providers for API features and contribute to the project through pull requests or discussions. The tool is licensed under the MIT License.

Heygem is an open-source, affordable alternative to Heygen, offering a fully offline video synthesis tool for Windows systems. It enables precise appearance and voice cloning, allowing users to digitalize their image and drive virtual avatars through text and voice for video production. With core features like efficient video synthesis and multi-language support, Heygem ensures a user-friendly experience with fully offline operation and support for multiple models. The tool leverages advanced AI algorithms for voice cloning, automatic speech recognition, and computer vision technology to enhance the virtual avatar's performance and synchronization.

MCP-researcher Server is a tool that serves as your research assistant inside Cline, utilizing Perplexity's Sonar Pro API to access documentation, create API routes, and check for deprecated code. It includes Chain of Thought Reasoning and local chat history through SQLite. The tool offers functionalities like general search queries, retrieving documentation, finding APIs, and analyzing deprecated code. Installation can be done via Smithery or manually by cloning the repository, installing dependencies, and setting up the Perplexity API key and server configurations for Claude Desktop and Cline.

Fragments is an open-source tool that leverages Anthropic's Claude Artifacts, Vercel v0, and GPT Engineer. It is powered by E2B Sandbox SDK and Code Interpreter SDK, allowing secure execution of AI-generated code. The tool is based on Next.js 14, shadcn/ui, TailwindCSS, and Vercel AI SDK. Users can stream in the UI, install packages from npm and pip, and add custom stacks and LLM providers. Fragments enables users to build web apps with Python interpreter, Next.js, Vue.js, Streamlit, and Gradio, utilizing providers like OpenAI, Anthropic, Google AI, and more.

ZenML is an extensible, open-source MLOps framework for creating portable, production-ready machine learning pipelines. By decoupling infrastructure from code, ZenML enables developers across your organization to collaborate more effectively as they develop to production.

Iceburg CRM is a metadata driven CRM with AI abilities that allows users to quickly prototype any CRM. It offers features like metadata creations, import/export in multiple formats, field validation, themes, role permissions, calendar, audit logs, API, workflow, field level relationships, module level relationships, and more. Created with Vue 3 for the frontend, Laravel 10 for the backend, Tailwinds with DaisyUI plugin, and Inertia for routing. Users can install default, admin panel, core, custom, or AI versions. The tool supports AI Assist for module data suggestions and provides API endpoints for CRM modules, search, specific module data, record updates, and deletions. Iceburg CRM also includes themes, custom field types, calendar, datalets, workflow, roles and permissions, import/export functionality, and custom seeding options.

Trapster Community is a low-interaction honeypot designed for internal networks or credential capture. It monitors and detects suspicious activities, providing deceptive security layer. Features include mimicking network services, asynchronous framework, easy configuration, expandable services, and HTTP honeypot engine with AI capabilities. Supported protocols include DNS, HTTP/HTTPS, FTP, LDAP, MSSQL, POSTGRES, RDP, SNMP, SSH, TELNET, VNC, and RSYNC. The tool generates various types of logs and offers HTTP engine with AI capabilities to emulate websites using YAML configuration. Contributions are welcome under AGPLv3+ license.

Search-R1 is a tool that trains large language models (LLMs) to reason and call a search engine using reinforcement learning. It is a reproduction of DeepSeek-R1 methods for training reasoning and searching interleaved LLMs, built upon veRL. Through rule-based outcome reward, the base LLM develops reasoning and search engine calling abilities independently. Users can train LLMs on their own datasets and search engines, with preliminary results showing improved performance in search engine calling and reasoning tasks.

Redis Vector Library (RedisVL) is a Python client library for building AI applications on top of Redis. It provides a high-level interface for managing vector indexes, performing vector search, and integrating with popular embedding models and providers. RedisVL is designed to make it easy for developers to build and deploy AI applications that leverage the speed, flexibility, and reliability of Redis.

The Strands Agents MCP Server is a model-driven approach to building AI agents in just a few lines of code. It provides curated documentation access to GenAI tools via llms.txt files, enabling AI coding assistants to search and retrieve relevant documentation with intelligent ranking. Features include smart document search, curated content indexing, on-demand fetching, snippet generation, and real URL support. The server can be used with various applications that support MCP servers, such as Amazon Q Developer CLI, Anthropic Claude Code, Cline, and Cursor. Users can quickly test the MCP server using the MCP Inspector and follow the provided steps to configure their MCP client and start using the documentation tools. The project welcomes contributions and is licensed under the Apache License 2.0.

The 'yu-picture' project is an educational project that provides complete video tutorials, text tutorials, resume writing, interview question solutions, and Q&A services to help you improve your project skills and enhance your resume. It is an enterprise-level intelligent collaborative cloud image library platform based on Vue 3 + Spring Boot + COS + WebSocket. The platform has a wide range of applications, including public image uploading and retrieval, image analysis for administrators, private image management for individual users, and real-time collaborative image editing for enterprises. The project covers file management, content retrieval, permission control, and real-time collaboration, using various programming concepts, architectural design methods, and optimization strategies to ensure high-speed iteration and stable operation.

TiDB.AI is a conversational search RAG (Retrieval-Augmented Generation) app based on TiDB Serverless Vector Storage. It provides an out-of-the-box and embeddable QA robot experience based on knowledge from official and documentation sites. The platform features a Perplexity-style Conversational Search page with an advanced built-in website crawler for comprehensive coverage. Users can integrate an embeddable JavaScript snippet into their website for instant responses to product-related queries. The tech stack includes Next.js, TypeScript, Tailwind CSS, shadcn/ui for design, TiDB for database storage, Kysely for SQL query building, NextAuth.js for authentication, Vercel for deployments, and LlamaIndex for the RAG framework. TiDB.AI is open-source under the Apache License, Version 2.0.

RAG-Powered Odoo Documentation Assistant is a comprehensive documentation processing and chat system that converts Odoo's documentation to a searchable knowledge base with an AI-powered chat interface. It supports multiple Odoo versions (16.0, 17.0, 18.0) and provides semantic search capabilities powered by OpenAI embeddings. The tool automates the conversion of RST to Markdown, offers real-time semantic search, context-aware AI-powered chat responses, and multi-version support. It includes a Streamlit-based web UI, REST API for programmatic access, and a CLI for document processing and chat. The system operates through a pipeline of data processing steps and an interface layer for UI and API access to the knowledge base.

GitMCP is a free, open-source service that transforms any GitHub project into a remote Model Context Protocol (MCP) endpoint, allowing AI assistants to access project documentation effortlessly. It empowers AI with semantic search capabilities, requires zero setup, is completely free and private, and serves as a bridge between GitHub repositories and AI assistants.

Code Puppy is an AI-powered code generation agent designed to understand programming tasks, generate high-quality code, and explain its reasoning. It supports multi-language code generation, interactive CLI, and detailed code explanations. The tool requires Python 3.9+ and API keys for various models like GPT, Google's Gemini, Cerebras, and Claude. It also integrates with MCP servers for advanced features like code search and documentation lookups. Users can create custom JSON agents for specialized tasks and access a variety of tools for file management, code execution, and reasoning sharing.

UsageRules is a development tool for Elixir projects that helps gather and consolidate usage rules from dependencies to provide to LLM agents. It provides pre-built usage rules for Elixir and a powerful documentation search task for hexdocs. The tool scans project dependencies, looks for `usage-rules.md` files, consolidates rules into a target file, and maintains sections that can be updated independently. It is useful for projects using frameworks like Ash, Phoenix, or other packages that provide specific usage guidelines, coding patterns, or best practices.

The Ollama R library provides an easy way to integrate R with Ollama for running language models locally on your machine. It supports working with standard data structures for different LLMs, offers various output formats, and enables integration with other libraries/tools. The library uses the Ollama REST API and requires the Ollama app to be installed, with GPU support for accelerating LLM inference. It is inspired by Ollama Python and JavaScript libraries, making it familiar for users of those languages. The installation process involves downloading the Ollama app, installing the 'ollamar' package, and starting the local server. Example usage includes testing connection, downloading models, generating responses, and listing available models.

**Prompt flow** is a suite of development tools designed to streamline the end-to-end development cycle of LLM-based AI applications, from ideation, prototyping, testing, evaluation to production deployment and monitoring. It makes prompt engineering much easier and enables you to build LLM apps with production quality.

DeepEval is a simple-to-use, open-source LLM evaluation framework specialized for unit testing LLM outputs. It incorporates various metrics such as G-Eval, hallucination, answer relevancy, RAGAS, etc., and runs locally on your machine for evaluation. It provides a wide range of ready-to-use evaluation metrics, allows for creating custom metrics, integrates with any CI/CD environment, and enables benchmarking LLMs on popular benchmarks. DeepEval is designed for evaluating RAG and fine-tuning applications, helping users optimize hyperparameters, prevent prompt drifting, and transition from OpenAI to hosting their own Llama2 with confidence.

MegaDetector is an AI model that identifies animals, people, and vehicles in camera trap images (which also makes it useful for eliminating blank images). This model is trained on several million images from a variety of ecosystems. MegaDetector is just one of many tools that aims to make conservation biologists more efficient with AI. If you want to learn about other ways to use AI to accelerate camera trap workflows, check out our of the field, affectionately titled "Everything I know about machine learning and camera traps".

LeapfrogAI is a self-hosted AI platform designed to be deployed in air-gapped resource-constrained environments. It brings sophisticated AI solutions to these environments by hosting all the necessary components of an AI stack, including vector databases, model backends, API, and UI. LeapfrogAI's API closely matches that of OpenAI, allowing tools built for OpenAI/ChatGPT to function seamlessly with a LeapfrogAI backend. It provides several backends for various use cases, including llama-cpp-python, whisper, text-embeddings, and vllm. LeapfrogAI leverages Chainguard's apko to harden base python images, ensuring the latest supported Python versions are used by the other components of the stack. The LeapfrogAI SDK provides a standard set of protobuffs and python utilities for implementing backends and gRPC. LeapfrogAI offers UI options for common use-cases like chat, summarization, and transcription. It can be deployed and run locally via UDS and Kubernetes, built out using Zarf packages. LeapfrogAI is supported by a community of users and contributors, including Defense Unicorns, Beast Code, Chainguard, Exovera, Hypergiant, Pulze, SOSi, United States Navy, United States Air Force, and United States Space Force.

This Docker image for LLaVA (Large Language and Vision Assistant) provides a convenient way to run LLaVA locally or on RunPod. LLaVA is a powerful AI tool that combines natural language processing and computer vision capabilities. With this Docker image, you can easily access LLaVA's functionalities for various tasks, including image captioning, visual question answering, text summarization, and more. The image comes pre-installed with LLaVA v1.2.0, Torch 2.1.2, xformers 0.0.23.post1, and other necessary dependencies. You can customize the model used by setting the MODEL environment variable. The image also includes a Jupyter Lab environment for interactive development and exploration. Overall, this Docker image offers a comprehensive and user-friendly platform for leveraging LLaVA's capabilities.

The 'carrot' repository on GitHub provides a list of free and user-friendly ChatGPT mirror sites for easy access. The repository includes sponsored sites offering various GPT models and services. Users can find and share sites, report errors, and access stable and recommended sites for ChatGPT usage. The repository also includes a detailed list of ChatGPT sites, their features, and accessibility options, making it a valuable resource for ChatGPT users seeking free and unlimited GPT services.

TrustLLM is a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions. Specifically, we first propose a set of principles for trustworthy LLMs that span eight different dimensions. Based on these principles, we further establish a benchmark across six dimensions including truthfulness, safety, fairness, robustness, privacy, and machine ethics. We then present a study evaluating 16 mainstream LLMs in TrustLLM, consisting of over 30 datasets. The document explains how to use the trustllm python package to help you assess the performance of your LLM in trustworthiness more quickly. For more details about TrustLLM, please refer to project website.

AI-YinMei is an AI virtual anchor Vtuber development tool (N card version). It supports fastgpt knowledge base chat dialogue, a complete set of solutions for LLM large language models: [fastgpt] + [one-api] + [Xinference], supports docking bilibili live broadcast barrage reply and entering live broadcast welcome speech, supports Microsoft edge-tts speech synthesis, supports Bert-VITS2 speech synthesis, supports GPT-SoVITS speech synthesis, supports expression control Vtuber Studio, supports painting stable-diffusion-webui output OBS live broadcast room, supports painting picture pornography public-NSFW-y-distinguish, supports search and image search service duckduckgo (requires magic Internet access), supports image search service Baidu image search (no magic Internet access), supports AI reply chat box [html plug-in], supports AI singing Auto-Convert-Music, supports playlist [html plug-in], supports dancing function, supports expression video playback, supports head touching action, supports gift smashing action, supports singing automatic start dancing function, chat and singing automatic cycle swing action, supports multi scene switching, background music switching, day and night automatic switching scene, supports open singing and painting, let AI automatically judge the content.